2026· SOFT MEASUREMENTS AND COMPUTING· Vol 2/5, pp. 41-50· 0 citations
TL;DR
It is substantiated that bridging this gap requires not targeted measures, but a systemic transformation of approaches to personnel management – the transition from role models to skill models, large-scale retraining and redesign of workplaces in the logic of human-machine cooperation.
Abstract
The article examines the fundamental role of human capital as a key factor in the successful development and implementation of artificial intelligence (AI) technologies. Based on the analysis of empirical research and theoretical concepts, it is shown that a high level of education and qualifications is a prerequisite for the diffusion of AI technologies, explaining up to a third of the differences in the pace of their implementation between countries and industries. At the same time, the deep paradox of the current moment is revealed: rapid automation generates redundancy of personnel in traditional roles, while the shortage of specialists with critical AI competencies reaches 40-60% or more. The article substantiates that bridging this gap requires not targeted measures, but a systemic transformation of approaches to personnel management – the transition from role models to skill models, large-scale retraining and redesign of workplaces in the logic of human-machine cooperation.
The article explores the potential of using artificial intelligence technologies to assess and predict the development of human capital in an organization, with an emphasis not on technological aspects per se, but on a conceptual understanding of how AI is transforming approaches to personnel management, creating opportunities for the transition from reactive, historical data-based practices to proactive, predictive talent management systems, and how this preserves the central role of human judgment in interpreting and validating the results of algorithmic analysis. The relevance of the study is due to the rapid spread of AI technologies in the field of human resource management, where, according to empirical studies, 89 percent of organizations intend to maintain or increase investments in AI despite economic uncertainty, and hybrid skills combining technical competence with developed "soft" skills such as critical thinking, adaptability, and emotional intelligence, the evaluation of which remains a challenging task for algorithmic systems. In contrast to the work focused on the development of specific algorithms or on regulatory issues of AI ethics, the present study focuses on the systematization of modern approaches to the use of AI in the assessment and forecasting of human capital, on identifying key areas of its application, including forecasting staff turnover, human resource assessment and long-term workforce planning, as well as on the justification of the need for hybrid models that combine algorithmic efficiency with human judgment.
D. Kurazova, Z. Troska, L. Tochieva· SOFT MEASUREMENTS AND COMPUT...· 0 citations
The article explores the transformative role of artificial intelligence technologies in three key functional areas of human capital management – recruitment, evaluation and employee development, with an emphasis on systematizing theoretical and applied approaches to integrating AI into personnel processes, as well as analyzing both the strategic advantages and ethical and organizational limitations of algorithmization of personnel management. The relevance of the study is due to the fundamental transformation of HR work under the influence of large language models and machine learning methods, when organizations are increasingly turning to AI to solve routine tasks, from automatic resume screening to forecasting turnover and personalization of development programs, and, according to systematic reviews, 33 percent of organizations have already implemented AI tools in talent management, however only 16 percent use these technologies optimally to achieve organizational results.
Petimat T. Gehaeva, O. Yanova, A. Kostoeva· SOFT MEASUREMENTS AND COMPUT...· 0 citations
This article examines the impact of the introduction of artificial intelligence on the structure and quality of human capital in the digital economy. The relevance of the research is due to the fundamental transformation of the labor market caused by the rapid spread of AI technologies, which creates both new opportunities for increasing labor productivity and serious challenges for the system of vocational education and retraining. Unlike the works focused on the technological aspects of AI or on macroeconomic employment forecasts, the present study focuses on structural changes in human capital – the transformation of skill requirements, the redistribution between cognitive and non-cognitive competencies, as well as the adaptive strategies of employees and firms. The purpose of the work is to systematize modern empirical and theoretical studies of the impact of AI on human capital and to substantiate the directions of its qualitative transformation.
Svetlana A. Gusarova, T. Skryl, D. Kurazova· SOFT MEASUREMENTS AND COMPUT...· 0 citations
This research paper reviews the three mechanisms through which artificial intelligence affects the labour market: substitution, creation, and augmentation. Unlike the previous wave of automation, artificial intelligence can perform both manual routine operations and cognitive tasks that require knowledge, thereby expanding its effects on employment. The paper provides evidence that AI can substitute codifiable tasks, create jobs in AI and the digital economy, and increase employee productivity through human-machine cooperation. Nevertheless, these AI effects are not equally distributed. Changes in skills demand due to the introduction of artificial intelligence manifest as increased value placed on skills such as digital literacy, data processing and analysis, problem-solving across disciplines, and lifelong learning, while routine jobs and entry-level positions become more vulnerable. Moreover, changes in employment expectations due to AI affect students and young workers, generating both anxiety and increased motivation for skill development. Previous studies show that AI changes tasks rather than occupations, but differ in their assessments of its employment effects.
Hongjie Chen, Zhenwei Tang· Journal of Applied Economics...· 0 citations
Background and Aim of Study: The current stage of artificial intelligence (AI) development is characterised by the unpredictability and emergent nature of models, the hidden capabilities of which are not always recognised by developers. The widespread integration of digital products, coupled with the emergent nature of this process, inevitably lead to the creation of general artificial intelligence (AGI). In this context, identifying mechanisms that enable society to interact safely with the latest technological systems is becoming an increasingly important task.
The aim of the study: to identify key trends in the development of artificial intelligence in contemporary society and to predict its impact on the transformation of the educational environment as a dominant factor in the digital adaptation of the younger generation.
Material and Methods: The study is grounded in a systems approach, employing theoretical methods of analysis, synthesis, induction, deduction, abstraction, comparison, systematization, and data interpretation.
Results: The present study provided a theoretical basis for the principle of responsibility for implementing and using artificial intelligence. The problem of anthropomorphism and human attachment to technologies was identified. It has been confirmed that the key risk does not stem from the use of AI itself, but rather from the environment in which it operates. It has also been established that the younger generation is the most adaptable demographic group in the process of acculturation to AI. The study identified four main vectors of AI’s influence on the transformation of the educational environment: 1) a paradigm shift: from digitalisation to an “AI-centred educational ecosystem”; 2) a radical change in the roles of stakeholders in education; 3) forming of new ethical and legal literacy among young people; 4) psychological adaptation and cognitive symbiosis.
Conclusions: Long-term forecasting confirms that institutional transformation in education and the transition to AI-centred ecosystems are inevitable. Transforming the teacher into a mentor and facilitator will equip students with adaptive autonomy and change the nature of the subject-subject relationship. The education sector plays a crucial role in preparing society to use AI responsibly for the benefit of humanity.
Y. Melnyk, I. Pypenko· International Journal of Sci...· 0 citations
The article analyzes the most competitive segment of the global labor market – the market of highly qualified specialists, who are particularly in demand in the context of the development of artificial intelligence. There is an interest in specialists with knowledge of artificial intelligence technologies who are able to develop, implement and optimize various AI solutions. New opportunities are opening up for them to simultaneously work on several projects in different countries using online platforms.
The analysis showed that with the development of AI, new professions appear, while traditional ones change or disappear. The degree of coverage of AI jobs varies between developed and developing countries. The working-age population of developed countries is more widely covered by AI than in developing countries, which increases the gap between countries in economic development.
In order to achieve technological sovereignty in Russia, where AI plays a key role, measures are being proposed to regulate the labor market in order to provide the Russian economy with the necessary specialists.